CMP 414/765 course repository for Spring 2022 semester

Overview

CMP414/765: Artificial Intelligence

Spring2021

This is the GitHub repository for course CMP 414/765: Artificial Intelligence taught at The City University of New York, Lehman College, in Spring 2022. Each week the instructor will upload one notebook that will be used in class.

View A Notebook

Simply click the file name on the list. Jupyter notebooks can be directly viewed on GitHub website.

Edit A Notebook

To edit a notebook from this repository, please click the "Open in Colab" button at the beginning. Students should be able to open the notebook in Google Colaboratory. Google Colab is a free Jupyter notebook environment that runs in the Google Cloud. An edited notebook can be saved to either Google Drive or another GitHub repository. Note that students are not able to save their edited notebooks to this repository.

It is expected that students follows each class by completing their version of the notebook. This includes:

  • Execute existing code cells to show expected results.
  • Complete exercises contained in the notebook.
  • Add new cells following the professor's instructions.

Save An Edited Notebook

To save the edited notebook in Google Drive, please click "File" -> "Save a copy in Drive". Google login is required.

To save the edited notebook in a GitHub repository, please click "File" -> "Save a copy in GitHub". GitHub login is requried.

To save the edited notebook as a PDF file, please click "File" -> "Print". Choose "Print as a PDF" in the pop-up window and click "Save".

For homework submission, a PDF file is required since other formats may not be properly displayed on Blackboard.

Owner
ch00226855
Liang Zhao, Assistant Professor at Department of Computer Science, Lehman College, City University of New York
ch00226855
UV matrix decompostion using movielens dataset

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Beyond imagenet attack (accepted by ICLR 2022) towards crafting adversarial examples for black-box domains.

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Improving Transferability of Representations via Augmentation-Aware Self-Supervision

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A library for differentiable nonlinear optimization.

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Official PyTorch implementation of PICCOLO: Point-Cloud Centric Omnidirectional Localization (ICCV 2021)

Official PyTorch implementation of PICCOLO: Point-Cloud Centric Omnidirectional Localization (ICCV 2021)

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CARL provides highly configurable contextual extensions to several well-known RL environments.

CARL (context adaptive RL) provides highly configurable contextual extensions to several well-known RL environments.

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Official implementation for paper: A Latent Transformer for Disentangled Face Editing in Images and Videos.

A Latent Transformer for Disentangled Face Editing in Images and Videos Official implementation for paper: A Latent Transformer for Disentangled Face

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PyTorch implementation of Soft-DTW: a Differentiable Loss Function for Time-Series in CUDA

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Road Crack Detection Using Deep Learning Methods

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GPU Accelerated Non-rigid ICP for surface registration

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BitPack is a practical tool to efficiently save ultra-low precision/mixed-precision quantized models.

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Learned image compression

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